4.It is not necessary to have a target variable for applying dimensionality reductionalgorithms.A. Supervised learning is the machine learning task of learning These Machine Learning Multiple Choice Questions (MCQ) should be practiced to improve the Data Science skills required for various interviews (campus interview, walk-in interview, company interview), placements, entrance exams and other competitive examinations. These short objective type questions with answers are very important for Board exams as well as competitive exams. 13. ... determine a best set of input attributes for supervised learning; evaluate the likely performance of a supervised learner model; Question Context 34:Consider the following data where one input(X) and one output(Y) is given. 61. Q. of desired clusters5. Supervised learning as the name indicates the presence of a supervisor as a teacher. Suppose you are using a bagging based algorithm say a RandomForest in model building. B) Some of the coefficient will be approaching to zero but not absolute zeroC) Both A and B depending on the situationD) None of theseSolution: (A)As already discussed, lasso applies absolute penalty, so some of the coefficients will become zero. The multiple coefficient of determination is computed bya) dividing SSR by SSTb) dividing SST by SSRc) dividing SST by SSEd) none of the aboveAns : Solution C, 20. Now, think that you increase the complexity (or degree of polynomial of this kernel). 16. 3. For a low cost, you aim for a smooth decision surface and for a higher cost, you aim to classify more points correctly. 24. Suppose, above decision boundaries were generated for the different value of regularization. Random Forest is use for classification whereas Gradient Boosting is use for regression task3. 17. For clusters with arbitrary shapes, these algorithms What would you think how many times we need to train SVM in such case?A) 1B) 2C) 3D) 4Solution: ATraining the SVM only one time would give you appropriate results. For a multiple regression model, SST = 200 and SSE = 50. High entropy To test our linear regressor, we split the data in training set and test set randomly.32. TRUEB. The density-based b) read only. Suppose, You applied a Logistic Regression model on a given data and got a training accuracy X and testing accuracy Y. None of theseAns Solution: (A) If a columns have too many missing values, (say 99%) then we can remove such columns. What Is The Internet Of Things and How IOT Works, Antsle Review: Virtual Machine Appliance For Developers, Top 10 Apps For Small Scale Business Entrepreneurs, 9 Ways to Fix Wifi Keeps Disconnecting and Recon­nect­ing Issue. Machine Learning MCQs Questions And Answers. Unsupervised learning does not use output data. Question Context 24-26:Suppose you have fitted a complex regression model on a dataset. Naïve Bayes and Support Vector Machine. Which of the followingconclusion do you make about this situation?A) Since the there is a relationship means our model is not goodB) Since the there is a relationship means our model is goodC) Can’t sayD) None of theseSolution: (A)There should not be any relationship between predicted values and residuals. Supervised learning can be divided into two categories: classification and regression. Which of the following option would you more likely to consider iterating SVM next time?A) You want to increase your data pointsB) You want to decrease your data pointsC) You will try to calculate more variablesD) You will try to reduce the featuresSolution: CThe best option here would be to create more features for the model. Which of the following option is true?A) Linear Regression errors values has to be normally distributed but in case of Logistic Regression it isnot the caseB) Logistic Regression errors values has to be normally distributed but in case of Linear Regression it isnot the caseC) Both Linear Regression and Logistic Regression error values have to be normally distributedD) Both Linear Regression and Logistic Regression error values have not to be normally distributedSolution:A, 53. Machine learning techniques differ from statistical techniques in that machine learning methodsa) typically assume an underlying distribution for the data.b) are better able to deal with missing and noisy data.c) are not able to explain their behavior.d) have trouble with large-sized datasets.Ans : Solution B. It contains a model that is able to predict with the help of a labeled dataset. Supervised learning is a simpler method. For data points to be in a cluster, they must be in a distance threshold to a core point2. 18. Supervised learning algorithm should have input variable (x) and an output variable (Y) for each example. Suppose you gave the correct answer in previous question. This section focuses on "Machine Learning" in Data Science. Attributes are 58. Regression trees are often used to model _______ data.a) Linearb) Nonlinearc) Categoricald) SymmetricalAns : Solution B, 38. In Random forest you can generate hundreds of trees (say T1, T2 …..Tn) and then aggregate the results of these tree. 18. 12. It is like learning under the guidance of a teacher; Training dataset is like a teacher which is used to train the machine; Model is trained on a pre-defined dataset before it starts making decisions when given new data; Bagging is the method for improving the performance by aggregating the results of weaklearnersA) 1B) 2C) 1 and 2D) None of theseAns Solution: CBoth options are true. The effectiveness of an SVM depends upon:A) Selection of KernelB) Kernel ParametersC) Soft Margin Parameter CD) All of the aboveSolution: DThe SVM effectiveness depends upon how you choose the basic 3 requirements mentioned above in such a way that it maximises your efficiency, reduces error and overfitting. PCA is a technique for reducing the dimensionality of According to this fact, what sizes of datasets are not best suited for SVM’s?A) Large datasetsB) Small datasetsC) Medium sized datasetsD) Size does not matterSolution: ADatasets which have a clear classification boundary will function best with SVM’s. How many possible different examples are there? classification algorithm for binary (two-class) and multi-class Several sets of data related to each other used to make decisions in machine learning algorithms. Individual tree is built on a subset of observations4. Unsupervised learning is computationally complex : Use of Data : Supervised learning model uses training data to learn a link between the input and the outputs. With Bayes classifier, missing data items area) treated as equal compares.b) treated as unequal compares.c) replaced with a default value.d) ignored.Ans : Solution B, 43. 9. 24. If V1 increases then V2 also increases2. 1. Removing columns with dissimilar data trendsD. machine learning quiz and MCQ questions with answers, data scientists interview, question and answers in MLE, linear regression, conditional probability, supervised ML algorithms, top 5 questions in Machine Learning If you are a data scientist, then you need to be good at Machine Learning – no two ways about it. 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